CN114859845A - Intelligent industrial data management system based on internet-of-things controller - Google Patents
Intelligent industrial data management system based on internet-of-things controller Download PDFInfo
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by the network communication
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention belongs to the field of industrial management, relates to a data processing technology, and is used for solving the problems of resource waste and equipment loss caused by the fact that the conventional industrial data management system mainly maintains equipment in a periodic maintenance mode, in particular to an intelligent industrial data management system based on an internet of things controller, which comprises an industrial management platform, wherein the industrial management platform is in communication connection with an equipment monitoring module, an abnormality analysis module, a fault analysis module and a storage module; the equipment monitoring module is used for monitoring and analyzing the running state of the industrial production equipment, acquiring vibration data, noise data and temperature data of a monitored object, carrying out numerical calculation to obtain a working coefficient of the monitored object, and marking the monitored object as a normal object or an abnormal object according to the numerical value of the working coefficient; the invention monitors the running state of the monitored object through the numerical value of the working coefficient, and feeds back the monitored object in time when the monitored object runs abnormally.
Description
Technical Field
The invention belongs to the field of industrial management, relates to a data processing technology, and particularly relates to an intelligent industrial data management system based on an internet-of-things controller.
Background
The intelligent industry is a new intelligent stage which continuously integrates various terminals with environment perception capability, a computing mode based on ubiquitous technology, mobile communication and the like into each link of industrial production, greatly improves the manufacturing efficiency, improves the product quality, reduces the product cost and the resource consumption, and improves the traditional industry.
The existing industrial data management system generally performs regular maintenance on industrial equipment, and performs periodic maintenance mainly in units of time, which causes resource waste and equipment loss, and determines the nature of an abnormality and the cause of the abnormality when the equipment is abnormally operated, which causes low efficiency in handling the abnormality of the equipment.
In view of the above technical problems, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an intelligent industrial data management system based on an internet-of-things controller, which is used for solving the problems of resource waste and equipment loss caused by the fact that the conventional industrial data management system mainly maintains equipment in a periodic maintenance mode;
the technical problems to be solved by the invention are as follows: how to provide an intelligent industrial data management system which can carry out maintenance and repair on equipment according to the running state of the equipment.
The purpose of the invention can be realized by the following technical scheme:
the intelligent industrial data management system based on the Internet of things controller comprises an industrial management platform, wherein the industrial management platform is in communication connection with an equipment monitoring module, an abnormality analysis module, a fault analysis module and a storage module;
the equipment monitoring module is used for monitoring and analyzing the running state of the industrial production equipment, marking the industrial production equipment as a monitored object, acquiring vibration data, noise data and temperature data of the monitored object, performing numerical calculation to obtain a working coefficient of the monitored object, marking the monitored object as a normal object or an abnormal object according to the numerical value of the working coefficient, and sending the abnormal object to the abnormality analysis module;
the abnormal analysis module performs abnormal analysis on the abnormal object after receiving the abnormal object to obtain an abnormal ratio and a floating coefficient of the abnormal object, and judges the abnormal object to be sporadic abnormal or habitual abnormal according to the numerical values of the abnormal ratio and the floating coefficient;
the fault analysis module is used for analyzing the abnormal reason of the habitual abnormal object.
As a preferred embodiment of the present invention, the vibration data of the monitored object is a vibration frequency value generated when the monitored object works; the noise data of the monitored object is a noise decibel value generated when the monitored object works; the temperature data of the monitored object is the temperature value of the air in the monitored object.
As a preferred embodiment of the present invention, a specific process for marking a monitored object as a normal object or an abnormal object includes: acquiring a working threshold value through a storage module, and comparing the working coefficient of the monitored object with the working threshold value:
if the working coefficient is smaller than the working threshold, judging that the working state of the monitored object meets the requirement, and marking the corresponding monitored object as a normal object;
and if the working coefficient is larger than or equal to the working threshold, judging that the working state of the monitored object does not meet the requirement, and marking the corresponding monitored object as an abnormal object.
As a preferred embodiment of the present invention, the process of obtaining the anomaly ratio and the floating coefficient includes: the method comprises the steps of marking the working time of an abnormal object in L1 days as analysis time, dividing the analysis time into a plurality of analysis periods, marking the maximum value of a working coefficient in the analysis periods as a working performance value of the analysis periods, marking the analysis periods with the working performance value not less than a working threshold value as abnormal periods, marking the ratio of the number of the abnormal periods to the number of the analysis periods as an abnormal ratio, establishing a working set of the working coefficient in the analysis periods, and carrying out variance calculation on the working set to obtain a floating coefficient of the abnormal object.
As a preferred embodiment of the present invention, the specific process of determining whether the abnormal object is an incidental abnormality or a habitual abnormality includes: acquiring an abnormal threshold and a floating threshold through a storage module, and respectively comparing an abnormal ratio and a floating coefficient of an abnormal object with the abnormal threshold and the floating threshold: if the anomaly ratio is less than or equal to the anomaly threshold value and the floating coefficient is less than or equal to the floating threshold value, judging that the abnormal object is sporadic anomaly, and sending an anomaly processing signal to the industrial management platform by the anomaly analysis module; otherwise, judging that the abnormal object is habitual abnormal, sending a maintenance signal to the industrial management platform by the abnormal analysis module, and sending the maintenance signal to the fault analysis module after receiving the maintenance signal by the abnormal analysis module.
As a preferred embodiment of the present invention, the specific process of analyzing the abnormality cause of the abnormal object with habitual abnormality by the fault analysis module includes: acquiring the environment temperature data, the environment humidity data and the environment dust data of the abnormal object; carrying out numerical calculation on the environment temperature data, the environment humidity data and the environment dust data of the abnormal object to obtain an environment coefficient of the abnormal object; obtaining an environment threshold through a storage module, and comparing the environment coefficient with the environment threshold: if the environmental coefficient is larger than or equal to the environmental threshold, judging that the cause of habitual abnormality of the abnormal object is related to the environment, sending an environmental regulation signal to the industrial management platform by the fault analysis module, and sending the environmental regulation signal to a mobile phone terminal of a manager by the industrial management platform after receiving the environmental regulation signal; and if the environment coefficient is smaller than the environment threshold value, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the environment, and performing depth detection on the abnormal object.
As a preferred embodiment of the present invention, the process of acquiring the ambient temperature data of the abnormal object includes: acquiring a temperature value and a proper temperature range of the outside air of the abnormal object, marking an average value of a maximum value and a minimum value of the proper temperature range as a temperature standard value, and marking an absolute value of a difference value between the temperature value and the temperature standard value of the outside air of the abnormal object as the ambient temperature data of the abnormal object;
the acquisition process of the ring wetting data of the abnormal object comprises the following steps: acquiring a humidity value and a proper humidity range of the outside air of the abnormal object, marking an average value of a maximum value and a minimum value of the proper humidity range as a humidity standard value, and marking an absolute value of a difference value between the humidity value and the humidity standard value as environmental humidity data;
the acquisition process of the ring dust data of the abnormal object comprises the following steps: and acquiring a dust concentration value of the air outside the abnormal object and marking the dust concentration value as the dust surrounding data of the abnormal object.
As a preferred embodiment of the present invention, the specific process of depth detection includes:
and (3) detecting the voltage of the abnormal object: acquiring the maximum value and the minimum value of the voltage of the abnormal object power supply line within L2 hours, wherein L2 is a numerical constant, and the numerical value of L2 is set by a manager; the difference value between the maximum voltage value and the minimum voltage value is marked as a transformation value, a voltage threshold value and a transformation threshold value are obtained through a storage module, and the maximum voltage value and the transformation value are respectively compared with the voltage threshold value and the transformation threshold value: if the maximum voltage value is smaller than the voltage threshold value and the transformation value is smaller than the transformation threshold value, judging that the cause of habitual abnormality of the abnormal object is irrelevant to the voltage of the power supply line, and detecting the current of the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the voltage of the power supply line, sending a voltage fault signal to the industrial management platform by the fault analysis module, and sending the voltage fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the voltage fault signal;
the specific process of detecting the current of the abnormal object comprises the following steps: acquiring the maximum value and the minimum value of the current of the abnormal object power supply line within L3 hours, wherein L3 is a numerical constant, and the numerical value of L3 is set by a manager; marking the difference value between the maximum value and the minimum value of the current as a variable current value, acquiring a current threshold value and a variable current threshold value through a storage module, and respectively comparing the maximum value and the variable current value with the current threshold value and the variable current threshold value: if the maximum current value is smaller than the current threshold and the variable current value is smaller than the variable current threshold, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the current of the power supply line, and carrying out three-phase detection on the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the current of the power supply line, sending a current fault signal to the industrial management platform by the fault analysis module, and sending the current fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the current fault signal;
the specific process of carrying out three-phase detection on the abnormal object comprises the following steps: acquiring the unbalance degree of an abnormal object, acquiring an unbalance range through a storage module, judging whether the unbalance degree of the abnormal object is within the unbalance range, if not, judging that the cause of habitual abnormality of the abnormal object is related to three-phase balance of a power supply line, sending a three-phase balance fault signal to an industrial management platform by a fault analysis module, and sending the three-phase balance fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the three-phase balance fault signal; if yes, the reason that the habitual abnormality of the abnormal object is judged to be irrelevant to the three-phase balance of the power supply line, the fault analysis module sends a mechanical fault signal to the industrial management platform, and the industrial management platform sends the mechanical fault signal to a mobile phone terminal of a manager after receiving the mechanical fault signal.
As a preferred embodiment of the present invention, the method for operating an intelligent industrial data management system based on an internet of things controller includes the following steps:
the method comprises the following steps: the equipment monitoring module monitors and analyzes the running state of the industrial production equipment to obtain a working coefficient of a monitored object, compares the working coefficient with a working threshold value and marks the monitored object as a normal object or an abnormal object according to a comparison result;
step two: the abnormal analysis module performs abnormal analysis on the abnormal object to obtain an abnormal ratio and a floating coefficient of the abnormal object, and judges whether the abnormal object is sporadic abnormality or habitual abnormality according to the numerical values of the abnormal ratio and the floating coefficient;
step three: the fault analysis module analyzes influence factors of an abnormal object with habitual abnormality, judges whether the habitual abnormality is related to the working environment of the equipment according to the value of the environment coefficient, deeply detects the abnormal object when the habitual abnormality is not related to the working environment of the equipment, and judges the cause of the habitual abnormality as a mechanical fault, a voltage fault, a current fault or a three-phase balance fault according to the result of the deep detection.
The invention has the following beneficial effects:
1. the working coefficient of the monitored object is obtained by monitoring and analyzing the vibration data, the noise data and the temperature data of the industrial production equipment, the running state of the monitored object is monitored according to the numerical value of the working coefficient, and the monitored object is fed back in time when running abnormity occurs, so that the normal running of the industrial production equipment is prevented from being influenced by the running of the industrial production equipment in an abnormal state;
2. the abnormal object is subjected to abnormal analysis to obtain an abnormal ratio and a floating coefficient, the abnormal property of the abnormal object is judged according to the numerical value of the abnormal ratio and the floating coefficient, whether the abnormal object has the abnormality happens accidentally or habitually is analyzed, the abnormal object which happens accidentally is subjected to abnormal processing, and the abnormal reason analysis is required for the abnormal object which happens habitually;
3. the environmental coefficient is obtained by analyzing the reason causing the habitual abnormality of the abnormal object, the numerical value of the environmental coefficient can judge whether the reason of the habitual abnormality of the abnormal object is the environmental abnormality or not, the voltage stability of the power supply line is detected when the reason of the habitual abnormality is unrelated to the environmental abnormality, the reason of the habitual abnormality is judged to be a mechanical fault or a line fault, the habitual abnormality can be treated in a targeted mode when the habitual abnormality is overhauled, and the overhauling and maintenance efficiency of the abnormal object is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, the intelligent industrial data management system based on the internet of things controller includes an industrial management platform, and the industrial management platform is in communication connection with an equipment monitoring module, an anomaly analysis module, a fault analysis module, and a storage module.
The equipment monitoring module is used for monitoring and analyzing the running state of the industrial production equipment: marking industrial production equipment as a monitoring object, and acquiring vibration data ZD, noise data ZS and temperature data WD of the monitoring object, wherein the vibration data ZD of the monitoring object is a vibration frequency value generated when the monitoring object works, and the vibration frequency value is directly acquired by a vibration sensor, the vibration sensor is one of key components in the testing technology, and the vibration sensor mainly receives mechanical quantity and converts the mechanical quantity into electric quantity proportional to the mechanical quantity; the noise data ZS of the monitored object is a noise decibel value generated when the monitored object works, the noise decibel value is directly acquired by a noise sensor, and the noise sensor is just a capacitance type electret microphone sensitive to sound arranged in the sensor, so that the sound wave enables an electret film in the microphone to vibrate to cause the change of capacitance and generate a tiny voltage which changes correspondingly to the capacitance, thereby realizing the conversion from an optical signal to an electric signal; the temperature data WD of the monitored object is the temperature value of the air in the monitored object, the temperature value is directly obtained by a temperature sensor, the temperature sensor is a sensor which can sense the temperature and convert the temperature into an available output signal, and the temperature sensor is the core part of a temperature measuring instrument and is various; obtaining a working coefficient GZ of the monitored object through GZ ═ alpha 1 × ZD + alpha 2 × ZS + alpha 3 × WD, wherein the working coefficient is a numerical value reflecting the stability degree of the monitored object during working, and the smaller the numerical value of the working coefficient, the higher the stability degree of the corresponding monitored object during working is; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 1 is more than alpha 2 and more than alpha 3 is more than 1; acquiring a working threshold GZmax through a storage module, and comparing the working coefficient GZ of the monitored object with the working threshold GZmax: if the working coefficient GZ is smaller than the working threshold GZmax, judging that the working state of the monitored object meets the requirement, and marking the corresponding monitored object as a normal object; if the working coefficient GZ is larger than or equal to the working threshold GZmax, judging that the working state of the monitored object does not meet the requirement, marking the corresponding monitored object as an abnormal object, and sending the abnormal object to an abnormal analysis module by the equipment monitoring module; the working coefficient of the monitored object is obtained by monitoring and analyzing the vibration data, the noise data and the temperature data of the industrial production equipment, the running state of the monitored object is monitored through the numerical value of the working coefficient, and the monitored object is fed back in time when running abnormity occurs, so that the normal running of the industrial production equipment is prevented from being influenced by the running of the industrial production equipment in the abnormal state.
After receiving the abnormal object, the abnormal analysis module performs abnormal analysis on the abnormal object: the working time of the abnormal object in L1 days is marked as analysis time, L1 is a numerical constant, and the numerical value of L1 is set by a manager; dividing the analysis duration into a plurality of analysis periods, marking the maximum value of the work coefficient in the analysis periods as the work expression value of the analysis periods, marking the analysis periods with the work expression value not less than the work threshold value as abnormal periods, marking the ratio of the number of the abnormal periods to the number of the analysis periods as an abnormal ratio, establishing a work set for the work coefficient of the analysis periods, carrying out variance calculation on the work set to obtain a floating coefficient of an abnormal object, obtaining the abnormal threshold value and the floating threshold value through a storage module, and comparing the abnormal ratio and the floating coefficient of the abnormal object with the abnormal threshold value and the floating threshold value respectively: if the anomaly ratio is less than or equal to the anomaly threshold value and the floating coefficient is less than or equal to the floating threshold value, judging that the abnormal object is sporadic anomaly, wherein the sporadic anomaly represents that the frequency of the abnormal object with abnormal operation in L1 days is low, and processing the abnormal object with the abnormal operation at the current time; the abnormality analysis module sends an abnormality processing signal to the industrial management platform; otherwise, judging that the abnormal object is habitual abnormality, wherein the habitual abnormality indicates that the abnormal object has high operation abnormality frequency within L1 days, troubleshooting and maintenance are needed on the abnormal object, the equipment fault is predicted through operation abnormal data of the equipment, and the fault reason causing the operation abnormality is diagnosed and analyzed in advance, so that the detection and maintenance efficiency can be accelerated while the equipment loss is reduced, the abnormality analysis module sends a maintenance signal to the industrial management platform, and the abnormality analysis module sends the maintenance signal to the fault analysis module after receiving the maintenance signal; the abnormal object is subjected to abnormal analysis to obtain an abnormal ratio and a floating coefficient, the abnormal property of the abnormal object is judged according to the numerical values of the abnormal ratio and the floating coefficient, whether the abnormal object has the abnormality or is habitually generated is analyzed, the abnormal object which happens occasionally is subjected to abnormal processing, and the abnormal reason analysis is required for the habitually generated abnormal object.
The fault analysis module analyzes the reasons causing the habitual abnormality of the abnormal object after receiving the maintenance signal: acquiring the environment temperature data HW, the environment humidity data HS and the environment dust data HC of the abnormal object, wherein the acquiring process of the environment temperature data HW of the abnormal object comprises the following steps: acquiring a temperature value and a proper temperature range of the external air of the abnormal object, marking an average value of a maximum value and a minimum value of the proper temperature range as a temperature standard value, and marking an absolute value of a difference value of the temperature value and the temperature standard value of the external air of the abnormal object as the ambient temperature data HW of the abnormal object; the acquisition process of the ring wetting data HS of the abnormal object comprises the following steps: acquiring a humidity value and a proper humidity range of external air of an abnormal object, wherein the humidity value is directly acquired by a humidity-sensitive sensor, and the humidity-sensitive sensor is a device which can sense external humidity change and convert the humidity into a useful signal through the physical or chemical property change of a device material; marking the average value of the maximum value and the minimum value of the suitable humidity range as a humidity standard value, and marking the absolute value of the difference value of the air humidity value and the humidity standard value as the environmental humidity data HS; the process of acquiring the ring dust data HC of the abnormal object includes: acquiring a dust concentration value of air outside an abnormal object and marking the dust concentration value as dust-surrounding data HC of the abnormal object, wherein the dust concentration value is directly acquired by a dust detector, the dust detector is an instrument for detecting the dust content in the air, the basic principle of the dust detector is that detection laser of an optical sensor is received by a photosensitive element after being scattered by dust particles and generates a pulse signal, the pulse signal is output and amplified, then digital signal processing is carried out, and a comparison result is expressed by different parameters by comparing with a standard particle signal; obtaining an environment coefficient HJ of the abnormal object through a formula HJ ═ beta 1 × HW + beta 2 × HS + beta 3 × HC, wherein the environment coefficient is a numerical value reflecting the abnormal degree of the working environment of the abnormal object, and the larger the numerical value of the environment coefficient is, the more abnormal the working environment of the corresponding abnormal object is; wherein beta 1 > beta 2 > beta 3 > 1; acquiring an environment threshold HJmax through a storage module, and comparing the environment coefficient HJ with the environment threshold HJmax: if the environmental coefficient HJ is larger than or equal to the environmental threshold HJmax, judging that the cause of habitual abnormality of the abnormal object is related to the environment, sending an environmental regulation signal to the industrial management platform by the fault analysis module, and sending the environmental regulation signal to a mobile phone terminal of a manager by the industrial management platform after receiving the environmental regulation signal; and if the environment coefficient HJ is smaller than the environment threshold value HJmax, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the environment, and performing depth detection on the abnormal object.
The specific process of performing depth detection on the abnormal object comprises the following steps:
and (3) detecting the voltage of the abnormal object: acquiring the maximum value and the minimum value of the voltage of the abnormal object power supply line within L2 hours, wherein L2 is a numerical constant, and the numerical value of L2 is set by a manager; the difference value between the maximum voltage value and the minimum voltage value is marked as a transformation value, a voltage threshold value and a transformation threshold value are obtained through a storage module, and the maximum voltage value and the transformation value are respectively compared with the voltage threshold value and the transformation threshold value: if the maximum voltage value is smaller than the voltage threshold and the transformation value is smaller than the transformation threshold, judging that the cause of habitual abnormality of the abnormal object is irrelevant to the voltage of the power supply line, and detecting the current of the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the voltage of the power supply line, sending a voltage fault signal to the industrial management platform by the fault analysis module, and sending the voltage fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the voltage fault signal;
the specific process of detecting the current of the abnormal object comprises the following steps: acquiring the maximum value and the minimum value of the current of the abnormal object power supply line within L3 hours, wherein L3 is a numerical constant, and the numerical value of L3 is set by a manager; marking the difference value between the maximum value and the minimum value of the current as a variable current value, acquiring a current threshold value and a variable current threshold value through a storage module, and respectively comparing the maximum value and the variable current value with the current threshold value and the variable current threshold value: if the maximum current value is smaller than the current threshold and the variable current value is smaller than the variable current threshold, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the current of the power supply line, and carrying out three-phase detection on the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the current of the power supply line, sending a current fault signal to the industrial management platform by the fault analysis module, and sending the current fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the current fault signal;
the specific process of carrying out three-phase detection on the abnormal object comprises the following steps: acquiring the unbalance degree of an abnormal object, acquiring an unbalance range through a storage module, judging whether the unbalance degree of the abnormal object is within the unbalance range, if not, judging that the habitual abnormality reason of the abnormal object is related to three-phase balance of a power supply line, sending a three-phase balance fault signal to an industrial management platform by a fault analysis module, and sending the three-phase balance fault signal to a mobile phone terminal of a manager after the industrial management platform receives the three-phase balance fault signal; if yes, the reason that the habitual abnormality of the abnormal object is judged to be irrelevant to the three-phase balance of the power supply line, the fault analysis module sends a mechanical fault signal to the industrial management platform, and the industrial management platform sends the mechanical fault signal to a mobile phone terminal of a manager after receiving the mechanical fault signal.
The environmental coefficient is obtained by analyzing the reason causing the habitual abnormality of the abnormal object, the numerical value of the environmental coefficient can judge whether the reason of the habitual abnormality of the abnormal object is the environmental abnormality or not, the voltage stability of the power supply line is detected when the reason of the habitual abnormality is unrelated to the environmental abnormality, the reason of the habitual abnormality is judged to be a mechanical fault or a line fault, the habitual abnormality can be treated in a targeted mode when the habitual abnormality is overhauled, and the overhauling and maintenance efficiency of the abnormal object is improved.
Example two
As shown in fig. 2, the intelligent industrial data management method based on the internet-of-things controller includes the following steps:
the method comprises the following steps: the equipment monitoring module monitors and analyzes the running state of the industrial production equipment to obtain a working coefficient of a monitored object, compares the working coefficient with a working threshold value, marks the monitored object as a normal object or an abnormal object according to a comparison result, and feeds back the monitored object in time when the monitored object runs abnormally so as to prevent the normal running of the industrial production equipment from being influenced when the industrial production equipment runs in an abnormal state;
step two: the abnormal analysis module is used for carrying out abnormal analysis on the abnormal object and obtaining an abnormal ratio and a floating coefficient of the abnormal object, judging the abnormal object to be sporadic abnormal or habitual abnormal according to the numerical values of the abnormal ratio and the floating coefficient, carrying out abnormal processing on the abnormal object which happens occasionally, and carrying out abnormal reason analysis on the abnormal object which happens habitually;
step three: the fault analysis module analyzes influence factors of the habitual abnormal object, judges whether the habitual abnormal object is related to the working environment of the equipment or not according to the numerical value of the environment coefficient, deeply detects the abnormal object when the habitual abnormal object is not related to the working environment of the equipment, judges the cause of the habitual abnormal object to be a mechanical fault, a voltage fault, a current fault or a three-phase balance fault according to the result of the deep detection, can specifically process the habitual abnormal object when the habitual abnormal object is overhauled, and accelerates the overhauling and maintenance efficiency of the abnormal object.
When the intelligent industrial data management system based on the Internet of things controller works, the equipment monitoring module monitors and analyzes the running state of industrial production equipment to obtain the working coefficient of a monitored object, compares the working coefficient with a working threshold value and marks the monitored object as a normal object or an abnormal object according to the comparison result; and finally, a fault analysis module is used for analyzing influence factors of the habitual abnormal object, and the cause of the habitual abnormality is judged to be a mechanical fault, a voltage fault, a current fault or a three-phase balance fault according to the result of depth detection.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: the formula GZ ═ α 1 × ZD + α 2 × ZS + α 3 × WD; collecting multiple groups of sample data and setting corresponding work coefficients for each group of sample data by a person skilled in the art; substituting the set working coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 3.86, 2.54 and 2.23;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the working coefficient preliminarily set by a person skilled in the art for each group of sample data; it is sufficient if the proportional relationship between the parameters and the quantized values is not affected, for example, the work coefficient is proportional to the value of the vibration data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. The intelligent industrial data management system based on the Internet of things controller comprises an industrial management platform, and is characterized in that the industrial management platform is in communication connection with an equipment monitoring module, an abnormality analysis module, a fault analysis module and a storage module;
the equipment monitoring module is used for monitoring and analyzing the running state of the industrial production equipment, marking the industrial production equipment as a monitored object, acquiring vibration data, noise data and temperature data of the monitored object, performing numerical calculation to obtain a working coefficient of the monitored object, marking the monitored object as a normal object or an abnormal object according to the numerical value of the working coefficient, and sending the abnormal object to the abnormality analysis module;
the abnormal analysis module performs abnormal analysis on the abnormal object after receiving the abnormal object to obtain an abnormal ratio and a floating coefficient of the abnormal object, and judges the abnormal object to be sporadic abnormal or habitual abnormal according to the numerical values of the abnormal ratio and the floating coefficient;
the fault analysis module is used for analyzing the abnormal reason of the habitual abnormal object.
2. The intelligent industrial data management system based on the internet-of-things controller according to claim 1, wherein the vibration data of the monitored object is a vibration frequency value generated when the monitored object works; the noise data of the monitored object is a noise decibel value generated when the monitored object works; the temperature data of the monitored object is the temperature value of the air in the monitored object.
3. The intelligent industrial data management system based on the internet-of-things controller according to claim 1, wherein the specific process of marking the monitored object as a normal object or an abnormal object comprises: acquiring a working threshold value through a storage module, and comparing the working coefficient of the monitored object with the working threshold value:
if the working coefficient is smaller than the working threshold, judging that the working state of the monitored object meets the requirement, and marking the corresponding monitored object as a normal object;
and if the working coefficient is greater than or equal to the working threshold, judging that the working state of the monitored object does not meet the requirement, and marking the corresponding monitored object as an abnormal object.
4. The intelligent industrial data management system based on the internet-of-things controller according to claim 1, wherein the obtaining process of the anomaly ratio and the floating coefficient comprises: the method comprises the steps of marking the working time of an abnormal object in L1 days as analysis time, dividing the analysis time into a plurality of analysis periods, marking the maximum value of a working coefficient in the analysis periods as a working performance value of the analysis periods, marking the analysis periods with the working performance value not less than a working threshold value as abnormal periods, marking the ratio of the number of the abnormal periods to the number of the analysis periods as an abnormal ratio, establishing a working set of the working coefficient in the analysis periods, and carrying out variance calculation on the working set to obtain a floating coefficient of the abnormal object.
5. The intelligent industrial data management system based on the internet-of-things controller according to claim 1, wherein the specific process of determining whether the abnormal object is an occasional abnormality or a habitual abnormality comprises: obtaining an abnormal threshold and a floating threshold through a storage module, and respectively comparing an abnormal ratio and a floating coefficient of an abnormal object with the abnormal threshold and the floating threshold: if the anomaly ratio is less than or equal to the anomaly threshold value and the floating coefficient is less than or equal to the floating threshold value, judging that the abnormal object is sporadic anomaly, and sending an anomaly processing signal to the industrial management platform by the anomaly analysis module; otherwise, judging that the abnormal object is habitual abnormal, sending a maintenance signal to the industrial management platform by the abnormal analysis module, and sending the maintenance signal to the fault analysis module after receiving the maintenance signal by the abnormal analysis module.
6. The intelligent industrial data management system based on the internet-of-things controller according to claim 1, wherein the specific process of analyzing the abnormal cause of the habitual abnormal object by the fault analysis module comprises: acquiring the environment temperature data, the environment humidity data and the environment dust data of the abnormal object; carrying out numerical calculation on the environment temperature data, the environment humidity data and the environment dust data of the abnormal object to obtain an environment coefficient of the abnormal object; obtaining an environment threshold through a storage module, and comparing the environment coefficient with the environment threshold: if the environmental coefficient is larger than or equal to the environmental threshold, judging that the cause of habitual abnormality of the abnormal object is related to the environment, sending an environmental regulation signal to the industrial management platform by the fault analysis module, and sending the environmental regulation signal to a mobile phone terminal of a manager by the industrial management platform after receiving the environmental regulation signal; and if the environment coefficient is smaller than the environment threshold value, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the environment, and performing depth detection on the abnormal object.
7. The intelligent industrial data management system based on the internet-of-things controller according to claim 6, wherein the process of acquiring the environmental temperature data of the abnormal object comprises: acquiring a temperature value and a proper temperature range of the outside air of the abnormal object, marking an average value of a maximum value and a minimum value of the proper temperature range as a temperature standard value, and marking an absolute value of a difference value between the temperature value and the temperature standard value of the outside air of the abnormal object as the ambient temperature data of the abnormal object;
the acquisition process of the ring wetting data of the abnormal object comprises the following steps: acquiring a humidity value and a proper humidity range of the outside air of the abnormal object, marking an average value of a maximum value and a minimum value of the proper humidity range as a humidity standard value, and marking an absolute value of a difference value between the humidity value and the humidity standard value as environmental humidity data;
the acquisition process of the ring dust data of the abnormal object comprises the following steps: and acquiring a dust concentration value of the air outside the abnormal object and marking the dust concentration value as the dust surrounding data of the abnormal object.
8. The intelligent industrial data management system based on the internet-of-things controller according to claim 6, wherein the specific process of deep detection comprises:
and (3) carrying out voltage detection on the abnormal object: acquiring the maximum value and the minimum value of the voltage of the abnormal object power supply line within L2 hours, wherein L2 is a numerical constant, and the numerical value of L2 is set by a manager; the difference value between the maximum voltage value and the minimum voltage value is marked as a transformation value, a voltage threshold value and a transformation threshold value are obtained through a storage module, and the maximum voltage value and the transformation value are respectively compared with the voltage threshold value and the transformation threshold value: if the maximum voltage value is smaller than the voltage threshold and the transformation value is smaller than the transformation threshold, judging that the cause of habitual abnormality of the abnormal object is irrelevant to the voltage of the power supply line, and detecting the current of the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the voltage of the power supply line, sending a voltage fault signal to the industrial management platform by the fault analysis module, and sending the voltage fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the voltage fault signal;
the specific process of detecting the current of the abnormal object comprises the following steps: acquiring the maximum value and the minimum value of the current of the abnormal object power supply line within L3 hours, wherein L3 is a numerical constant, and the numerical value of L3 is set by a manager; marking the difference value between the maximum value and the minimum value of the current as a variable current value, acquiring a current threshold value and a variable current threshold value through a storage module, and respectively comparing the maximum value and the variable current value with the current threshold value and the variable current threshold value: if the maximum current value is smaller than the current threshold and the variable current value is smaller than the variable current threshold, judging that the cause of the habitual abnormality of the abnormal object is irrelevant to the current of the power supply line, and carrying out three-phase detection on the abnormal object; otherwise, judging that the cause of habitual abnormality of the abnormal object is related to the current of the power supply line, sending a current fault signal to the industrial management platform by the fault analysis module, and sending the current fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the current fault signal;
the specific process of carrying out three-phase detection on the abnormal object comprises the following steps: acquiring the unbalance degree of an abnormal object, acquiring an unbalance range through a storage module, judging whether the unbalance degree of the abnormal object is within the unbalance range, if not, judging that the cause of habitual abnormality of the abnormal object is related to three-phase balance of a power supply line, sending a three-phase balance fault signal to an industrial management platform by a fault analysis module, and sending the three-phase balance fault signal to a mobile phone terminal of a manager by the industrial management platform after receiving the three-phase balance fault signal; if yes, the reason that the habitual abnormality of the abnormal object is judged to be irrelevant to the three-phase balance of the power supply line, the fault analysis module sends a mechanical fault signal to the industrial management platform, and the industrial management platform sends the mechanical fault signal to a mobile phone terminal of a manager after receiving the mechanical fault signal.
9. The intelligent industrial data management system based on the internet-of-things controller as claimed in any one of claims 1 to 8, wherein the working method of the intelligent industrial data management system based on the internet-of-things controller comprises the following steps:
the method comprises the following steps: the equipment monitoring module monitors and analyzes the running state of the industrial production equipment to obtain a working coefficient of a monitored object, compares the working coefficient with a working threshold value and marks the monitored object as a normal object or an abnormal object according to a comparison result;
step two: the abnormal analysis module performs abnormal analysis on the abnormal object to obtain an abnormal ratio and a floating coefficient of the abnormal object, and judges whether the abnormal object is sporadic abnormality or habitual abnormality according to the numerical values of the abnormal ratio and the floating coefficient;
step three: the fault analysis module analyzes influence factors of an abnormal object with habitual abnormality, judges whether the habitual abnormality is related to the working environment of the equipment according to the value of the environment coefficient, deeply detects the abnormal object when the habitual abnormality is not related to the working environment of the equipment, and judges the cause of the habitual abnormality as a mechanical fault, a voltage fault, a current fault or a three-phase balance fault according to the result of the deep detection.
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